Structured light triangulation is a method of choice for measuring shape in applications, including industrial automation, computer graphics, virtual reality, robot-human interaction, medical surgery, and computer vision. The shapes are measured from depth values.
Work in this field has been driven by two factors: first, reducing acquisition time, and second, increasing depth resolution. Conventional methods can measure 3D shapes at close to 1000 Hz with a depth resolution of more than 30 microns.
Most structured light methods make an important assumption: a scene only receives illumination directly from a light source. This assumption can only be fulfilled in a controlled laboratory or studio setting. For most real world scenes, this is not true. In several applications, the scene receives a significant amount of indirect or global illumination in the form of inter-reflections, sub-surface scattering, volumetric scattering, diffusion), which often is more than the direct illumination.
Conventional structured light methods do not account for these global light transport effects, resulting in significant and systematic errors in recovering shapes from depth values. Moreover, because of the systematic nature of these errors, it is hard to correct the errors in post-processing. It is not surprising then that those methods have been used on relatively simple scenes, devoid of global light transport effects.
Historically, global illumination has not been modeled in computer vision applications because computational models for global illumination are complex, even for relatively simple scenes.
More recently, it has been shown that the direct and global components of illumination can be efficiently separated. This led to a number of methods for performing structured light based shape reconstruction under global light transport. All of those methods rely on removing or reducing the effects of global light transport, either by polarization, high-frequency modulation of structured light patterns, or iteratively reducing the total illumination in the scene. Because those methods rely on obtaining the direct component, errors occur when the separation methods do not work.
For instance, both polarization and modulated illumination based separation fail to filter high frequency inter-reflections. Moreover, in scenes where the direct component is relatively low, for example, with highly translucent materials, removing the global component can be counter-productive, resulting in significant errors due to low signal-to-noise-ratio (SNR). In addition, all those methods have a significant over-head in terms of hardware setup and acquisition time.
Shape Recovery Under Global Light Transport
In computer vision applications, an iterative photometric approach reconstructs shape of Lambertian objects in the presence of inter-reflections. Another method explicitly separates global component from images for performing photometric stereo imaging. Depths can be recovered using projector defocus under global illumination effects. Another method use inter-reflections to resolve a bas-relief ambiguity inherent in shape-from-shading techniques. An active multi-view stereo technique uses high-frequency illumination as scene texture that is invariant to global illumination. Another method moves the camera or the scene to reduce the effects of global light transport in a structured light application.
It is desired to provide a structured light method and systems with a single camera and a projector without any moving parts to reconstruct transparent, opaque and translucent surfaces as well as surfaces that result in global illumination effects, such as inter-reflections and sub-surface scattering.